With the above sequences, we should cluster the sequences of the same person in all cameras.
To achieve effective and efficient cross-camera matching of the same person, we also utilize the person ReID features obtained by FastReID [1]}.
For each sequence, we first use a pose estimation model, i.e., HRNet https://github.com/HRNet/HRNet-Human-Pose-Estimation under the MIT License. [2]} fine-tuned on our dataset 4,000 images are labeled to fine-tune the pose estimator., to select a high-quality frame for cross-camera matching.
After that, we utilize FastReID to extract the features of the selected frames of all sequences.
Through an unsupervised clustering method, i.e., DBSCAN [3]}, we roughly obtain 5,336 clusters of sequences.
Then, we employ human annotators to filter out the outlier sequences in each group.
By discarding the groups containing only one sequence, we finally obtain 4,000 subjects and 25,309 sequences for generating the gait representations.
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